One of the key assets an enterprise has is the data it captures about its customers and their interactions with these customers. However, enterprises have been unable to properly leverage this data because of the lack of integration into a useful format. The need for data integration includes the problems of managing data quality and reliability, and the difficulties with data reconciliation and providing a unified view for data. Unfortunately, enterprises today address the need for coherent integrated data by building an integration on a poor data foundation.
The quality, reliability, unified view, and reconciliation problems are compounded by the distributed, heterogeneous, and dynamic nature of the data capture and change process, and the requirement that data entry must be a perfect match to be integrated. In addition, most companies fail to recognize that data consolidation through a periodic one-off batch process is ineffective. New data is constantly being added or changed from multiple operational sources such as web sites, marketing, and sales force activities.
The lack of integrated data drives a variety of business problems. Marketing, sales, finance, call-center, and service agents lack a complete understanding of customer history with the business and waste time trying to figure out which customer records to use or ignore. Sales and marketing expenses balloon as duplicate, dirty or incomplete data builds up in databases as a result of redundant or misdirected marketing campaigns. Opportunities to drive new revenues or increase profitability are lost when customers and interactions are not linked. Opportunities are also lost when cross-sell and up-sell recommendations are based on generic offers or inaccurate data about an individual customer. Operational, compliance, and credit risk increases as organizations lack understanding of the entire customer relationship. The lack of current and accurate information presents a problem particularly when communication channels require an immediate reaction to a customer response or inquiry.
Enterprises have invested billions of dollars in customer relationship management (CRM) applications to improve customer retention, reduce costs, and increase profitability. Yet, despite the need for data integration, it is rarely found as an operational system in today's business environments. For instance, many companies have purchased and implemented software applications that provide a solution for a single business function, product line or touch point. However, these solutions focus on using point tools for cleansing, matching, verification, and enhancement on a batch basis to create a single data source of the truth downstream from the data capture systems. This results in systems that are managed independently and do not interact or share data well with one another. Furthermore, these applications often have very different data models and means of tracking and reporting on user interactions and transactions, leaving companies with islands of difficult-to-reconcile data. Even after several of these CRM implementations, customer data typically resides in many different enterprise application data models.
Many solutions attempt to apply tools designed for other purposes to address the need for data integration. These tools include data warehouses (DW) for analytics, or enterprise application integration (EAI) tools for integrating processes between two applications. These poor fitting attempts have fallen short of delivering complete, trusted information in an operational setting. For instance, data warehousing efforts attempt to extract select data from multiple operational databases into a single collection of meaningful information. However, this process of data aggregation results are more for historical pattern detection and is often too stale and inaccurate to be useful within operational processes. Other solutions involve storing all relevant interaction data in an operational data store (ODS), necessarily resulting in application centric one off that is expensive to maintain.
Currently, it is a challenge of the enterprise to access relevant data and turn it into actionable information at the point of customer interaction. As mentioned previously, this is primarily due to the diversity of constantly changing, heterogeneous sources for capturing operational data. Further, enterprise applications cannot execute a business rule or logic independent of application channels since most data transformation rules between applications have been written in custom code. Currently, workflows are not triggered by customer events and business rules are uncoordinated and distributed among multiple systems. Fragmented customer views combined with multiple sets of uncoordinated software-enabled rules hinder an enterprise from providing sales and service to its customers.
As described above, enterprises require “a 360° view of the customer,” and need the most relevant information through existing applications. A complete view of the customer requires an ability to act in real time and to gather data from all applications and touch points. Previous approaches to fixing customer data quality and management problems have only focused on part of the data integration issue. These solutions do not meet the basic need of businesses for real time integration and management of high value (reliable) data. Thus, simultaneously leveraging all of the available data to obtain an up-to-date comprehensive view of any customer remains a significant data integration challenge for the enterprise.
Thus, there is a need in the art for a system that integrates data to create and maintain the best version of truth for that data and delivers that data to real-time operational setting, and across multiple data sources.